Intraoperative images such as radiographs and ultrasound are acquired during image-guided clinical interventions for localization, guidance, and verification of the operation. Target localization using human interpretation of intraoperative images can be a stressful, challenging task to the clinician, exacerbated by the time-sensitive constraints during clinical workflow. In such scenarios, solutions for intraoperative decision support could be valuable tools in assisting clinicians with the potential for improving clinical outcomes in image-guided clinical interventions and minimizing human error.
Preoperative images such as CT, MRI, PET, SPECT are acquired for diagnostic and planning purposes. These images are often superior in image quality and provide better 3D anatomical context to the clinician. Mapping information contained in preoperative imaging into the space of intraoperative images in real-time during procedure is a commonly used technique to convey clinically relevant information to assist the clinician. Since most intraoperative imaging modalities are available in 2D, such methods often require accurate and robust 3D-2D registration methods.
Preoperative and intraoperative imaging often contain complimentary details of anatomy. When multiple modalities are involved, such as preoperative MR images and intraoperative radiographs, there are usually drastic mismatches in image intensities/anatomical details caused primarily due to the differences in underlying imaging physics. Under such circumstances, mismatching content can drive 3D-2D registration to locally optimal solutions making it challenging to achieve accurate and robust performance.
It would therefore be advantageous to provide a 3D-2D registration method based on the segmentation of relevant anatomical regions in the 3D preoperative image. Recent advancements in image segmentation methods enable “intelligent” selection of gradients fulfilling a certain predefined objective criterion. Such capability allows to automatically extract relevant anatomical gradients from preoperative images and feed them into the registration. This approach aims to eliminate unnecessary, extraneous details contained in the preoperative image from the 3D-2D registration pipeline.